1#include "models.h"
2
3llm_build_olmo::llm_build_olmo(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
4 const int64_t n_embd_head = hparams.n_embd_head_v;
5
6 GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
7 GGML_ASSERT(n_embd_head == hparams.n_rot);
8
9 ggml_tensor * cur;
10 ggml_tensor * inpL;
11
12 inpL = build_inp_embd(tok_embd: model.tok_embd);
13
14 // inp_pos - contains the positions
15 ggml_tensor * inp_pos = build_inp_pos();
16
17 auto * inp_attn = build_attn_inp_kv();
18
19 ggml_tensor * inp_out_ids = build_inp_out_ids();
20
21 for (int il = 0; il < n_layer; ++il) {
22 ggml_tensor * inpSA = inpL;
23
24 // norm
25 cur = build_norm(cur: inpL,
26 NULL, NULL,
27 type: LLM_NORM, il);
28 cb(cur, name: "attn_norm", il);
29
30 // self-attention
31 {
32 // compute Q and K and RoPE them
33 ggml_tensor * Qcur = build_lora_mm(w: model.layers[il].wq, cur);
34 cb(cur: Qcur, name: "Qcur", il);
35 if (hparams.f_clamp_kqv > 0.0f) {
36 Qcur = ggml_clamp(ctx: ctx0, a: Qcur, min: -hparams.f_clamp_kqv, max: hparams.f_clamp_kqv);
37 cb(cur: Qcur, name: "Qcur", il);
38 }
39 ggml_tensor * Kcur = build_lora_mm(w: model.layers[il].wk, cur);
40 cb(cur: Kcur, name: "Kcur", il);
41 if (hparams.f_clamp_kqv > 0.0f) {
42 Kcur = ggml_clamp(ctx: ctx0, a: Kcur, min: -hparams.f_clamp_kqv, max: hparams.f_clamp_kqv);
43 cb(cur: Kcur, name: "Kcur", il);
44 }
45 ggml_tensor * Vcur = build_lora_mm(w: model.layers[il].wv, cur);
46 cb(cur: Vcur, name: "Vcur", il);
47 if (hparams.f_clamp_kqv > 0.0f) {
48 Vcur = ggml_clamp(ctx: ctx0, a: Vcur, min: -hparams.f_clamp_kqv, max: hparams.f_clamp_kqv);
49 cb(cur: Vcur, name: "Vcur", il);
50 }
51 Qcur = ggml_reshape_3d(ctx: ctx0, a: Qcur, ne0: n_embd_head, ne1: n_head, ne2: n_tokens);
52 Kcur = ggml_reshape_3d(ctx: ctx0, a: Kcur, ne0: n_embd_head, ne1: n_head_kv, ne2: n_tokens);
53 Vcur = ggml_reshape_3d(ctx: ctx0, a: Vcur, ne0: n_embd_head, ne1: n_head_kv, ne2: n_tokens);
54
55 Qcur = ggml_rope_ext(
56 ctx: ctx0, a: Qcur, b: inp_pos, c: nullptr,
57 n_dims: n_rot, mode: rope_type, n_ctx_orig, freq_base, freq_scale,
58 ext_factor, attn_factor, beta_fast, beta_slow
59 );
60
61 Kcur = ggml_rope_ext(
62 ctx: ctx0, a: Kcur, b: inp_pos, c: nullptr,
63 n_dims: n_rot, mode: rope_type, n_ctx_orig, freq_base, freq_scale,
64 ext_factor, attn_factor, beta_fast, beta_slow
65 );
66
67 cb(cur: Qcur, name: "Qcur", il);
68 cb(cur: Kcur, name: "Kcur", il);
69 cb(cur: Vcur, name: "Vcur", il);
70
71 cur = build_attn(inp: inp_attn,
72 wo: model.layers[il].wo, wo_b: nullptr,
73 q_cur: Qcur, k_cur: Kcur, v_cur: Vcur, kq_b: nullptr, sinks: nullptr, v_mla: nullptr, kq_scale: 1.0f/sqrtf(x: float(n_embd_head)), il);
74 }
75 if (il == n_layer - 1 && inp_out_ids) {
76 cur = ggml_get_rows(ctx: ctx0, a: cur, b: inp_out_ids);
77 inpSA = ggml_get_rows(ctx: ctx0, a: inpSA, b: inp_out_ids);
78 }
79 ggml_tensor * ffn_inp = ggml_add(ctx: ctx0, a: cur, b: inpSA);
80 cb(cur: ffn_inp, name: "ffn_inp", il);
81
82 // feed-forward network
83 cur = build_norm(cur: ffn_inp,
84 NULL, NULL,
85 type: LLM_NORM, il);
86 cb(cur, name: "ffn_norm", il);
87
88 cur = build_ffn(cur,
89 up: model.layers[il].ffn_up, NULL, NULL,
90 gate: model.layers[il].ffn_gate, NULL, NULL,
91 down: model.layers[il].ffn_down, NULL, NULL,
92 NULL,
93 type_op: LLM_FFN_SILU, type_gate: LLM_FFN_PAR, il);
94 cb(cur, name: "ffn_out", il);
95
96 cur = ggml_add(ctx: ctx0, a: cur, b: ffn_inp);
97 cb(cur, name: "ffn_out", il);
98
99 cur = build_cvec(cur, il);
100 cb(cur, name: "l_out", il);
101
102 // input for next layer
103 inpL = cur;
104 }
105 cur = inpL;
106
107 cur = build_norm(cur,
108 NULL, NULL,
109 type: LLM_NORM, il: -1);
110
111 cb(cur, name: "result_norm", il: -1);
112 res->t_embd = cur;
113
114 // lm_head
115 cur = build_lora_mm(w: model.output, cur);
116
117 cb(cur, name: "result_output", il: -1);
118 res->t_logits = cur;
119
120 ggml_build_forward_expand(cgraph: gf, tensor: cur);
121}
122